5,248 research outputs found
adPerf: Characterizing the Performance of Third-party Ads
Monetizing websites and web apps through online advertising is widespread in
the web ecosystem. The online advertising ecosystem nowadays forces publishers
to integrate ads from these third-party domains. On the one hand, this raises
several privacy and security concerns that are actively studied in recent
years. On the other hand, given the ability of today's browsers to load dynamic
web pages with complex animations and Javascript, online advertising has also
transformed and can have a significant impact on webpage performance. The
performance cost of online ads is critical since it eventually impacts user
satisfaction as well as their Internet bill and device energy consumption.
In this paper, we apply an in-depth and first-of-a-kind performance
evaluation of web ads. Unlike prior efforts that rely primarily on adblockers,
we perform a fine-grained analysis on the web browser's page loading process to
demystify the performance cost of web ads. We aim to characterize the cost by
every component of an ad, so the publisher, ad syndicate, and advertiser can
improve the ad's performance with detailed guidance. For this purpose, we
develop an infrastructure, adPerf, for the Chrome browser that classifies page
loading workloads into ad-related and main-content at the granularity of
browser activities (such as Javascript and Layout). Our evaluations show that
online advertising entails more than 15% of browser page loading workload and
approximately 88% of that is spent on JavaScript. We also track the sources and
delivery chain of web ads and analyze performance considering the origin of the
ad contents. We observe that 2 of the well-known third-party ad domains
contribute to 35% of the ads performance cost and surprisingly, top news
websites implicitly include unknown third-party ads which in some cases build
up to more than 37% of the ads performance cost
Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users
Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users\u27 experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via a blanket filtering operation, many websites are increasingly denying access if an ad blocker is active. Moreover, ad blockers often do not filter out internal ads injected by the websites themselves. Therefore, we devised an algorithm to automatically identify contextually deceptive ads on a web page. Specifically, we built a detection model that leverages a multi-modal combination of handcrafted and automatically extracted features to determine if a particular ad is contextually deceptive. Evaluations of the model on a representative test dataset and \u27in-the-wild\u27 random websites yielded F1 scores of 0.86 and 0.88, respectively
Online advertising: analysis of privacy threats and protection approaches
Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft
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